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Table 1.7 Intuitionistic
fuzzy decision matrix R ( 3 )
y 1
y 2
y 3
y 4
G 1
(0.5,0.4,0.1)
(0.6,0.4,0.0)
(0.3,0.7,0.0)
(0.2,0.7,0.1)
G 2
(0.5,0.4,0.1)
(0.7,0.3,0.0)
(0.6,0.4,0.0)
(0.5,0.3,0.2)
G 3
(0.6,0.2,0.2)
(0.3,0.5,0.2)
(0.3,0.5,0.2)
(0.9,0.1,0.0)
G 4
(0.3,0.5,0.2)
(0.5,0.5,0.0)
(0.6,0.2,0.2)
(0.6,0.4,0.0)
.
.
.
.
0
3909 0
3937 0
3909 0
3847
0
.
3892 0
.
3892 0
.
3921 0
.
3892
1 =
0
.
3811 0
.
3864 0
.
3829 0
.
4000
0
.
3864 0
.
3829 0
.
3909 0
.
3921
0
.
3046 0
.
2992 0
.
3046 0
.
3115
0
.
3015 0
.
3015 0
.
2960 0
.
3015
2 =
0
.
3055 0
.
3068 0
.
3085 0
.
3124
0
.
3068 0
.
3085 0
.
3046 0
.
3119
0
.
3046 0
.
3070 0
.
3046 0
.
3038
0
.
3093 0
.
3093 0
.
3119 0
.
3093
3 =
0
.
3134 0
.
3068 0
.
3085 0
.
2876
0
.
3068 0
.
3085 0
.
3046 0
.
2960
Step 2 Utilize the IFPWA operator ( 1.26 ) to aggregate all the individual intu-
itionistic fuzzy decision matrices R ( k ) = (
r ( k )
ij
) 4 × 4 (
=
,
,
)
k
1
2
3
into the collective
= (
r ij ) 4 × 4 :
intuitionistic fuzzy decision matrix R
(
0
.
4052
,
0
.
4938
,
0
.
1010
)(
0
.
5069
,
0
.
3670
,
0
.
1261
)(
0
.
2038
,
0
.
7962
,
0
.
0000
)
(
0
.
4466
,
0
.
4667
,
0
.
0867
)(
0
.
6086
,
0
.
3355
,
0
.
0559
)(
0
.
6183
,
0
.
2910
,
0
.
0907
)
R
=
(
0
.
6162
,
0
.
2334
,
0
.
1504
)(
0
.
2321
,
0
.
5288
,
0
.
2391
)(
0
.
3707
,
0
.
5005
,
0
.
1288
)
(
0
.
3323
,
0
.
5365
,
0
.
1312
)(
0
.
5332
,
0
.
3838
,
0
.
0830
)(
0
.
5062
,
0
.
2263
,
0
.
2675
)
(
0
.
2401
,
0
.
5862
,
0
.
1737
)
(
0
.
6070
,
0
.
2562
,
0
.
1368
)
(
0
.
6668
,
0
.
1242
,
0
.
2090
)
(
.
,
.
,
.
)
0
7214
0
1507
0
1279
in the j th column
of R by using the IFWA operator ( 1.57 ), and get the overall preference value r j
corresponding to the alternative y j :
Step 3 Aggregate all the preference values r ij (
j
=
1
,
2
,
3
,
4
)
r 1 = (
0
.
4642
,
0
.
4105
,
0
.
1253
),
r 2 = (
0
.
4857
,
0
.
3967
,
0
.
1176
)
r 3 = (
0
.
4322
,
0
.
4286
,
0
.
1392
),
r 4 = (
0
.
5710
,
0
.
2464
,
0
.
1826
)
Step 4 By Eq. ( 1.3 ), we calculate the scores of r j
(
j
=
1
,
2
,
3
,
4
)
respectively:
 
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